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One of the recent developments in South Asian historiography is the adoption of new approaches for each of its sub-disciplines. Historiographical scholarship has also discovered new avenues including exploration of the ignored and marginalized aspects of history. Muhammad Iqbal Chawla is one of such academicians who has left a strong mark on the field of historiography by making multifaceted and original contributions. These can be ascertained by a comprehensive assessment of his entire intellectual cum scholarly sojourn. Dr Chawla has attempted to create to put a new face in the varied field of historiography in Pakistan and he has largely succeeded in his endeavour. The main aim of this research paper is to assess his contribution. This objective can best be achieved by a thorough perusal of his contributions to historiography from multiple perspectives as the author has tried to do so in this article.

Purpose: The aim of this study was to analyze the moderating role of Perceived Organizational Support (POS) on the relationship among Job Stress (JS) and Job Burnout (JB) as well as Job Stress and Organizational Commitment (OC) under the pandemic situation. The direct relationship of Job Stress with Job Burnout and Organizational Commitment was also analyzed. Methodology: The study has been conducted on the 260 healthcare professionals (171 doctors & 89 medical support staff) of five well-established leading private hospitals of Lahore, a major city of Pakistan where diversity meets contemporary dynamics. An adopted questionnaire was utilized to collect the data from within working masse. To examine the relationship among the considered variable, regression as well as correlation analysis was deployed. After due analysis, it was found that JS is positively related to JB and it relates negatively with the OC. However, findings also revealed that POS does not moderate any of the relationships i.e., those of JS with OC & JB. Findings: The findings suggested that the pandemic situation proved to be stressful for the healthcare employees so as it led to burnout among them and reduced their commitment towards the organization. And that this stress due to pandemic situation was so that perceived support of employees from their organization could not alter their commitment in favor of the organization and also could not prevent the stress to reach the level of burnout among employees. Implications: The study offers practical implications to the managers. The burden on the shoulders of a manager is much more than he understands, constantly maintaining the balance between stress and encouragement is the ultimate duty of the manager. Along with managing the projects meeting the deadline with effectiveness, saving employees from the wear and tear mentally and physically is something of prime important enlisted in a manager’s duty. In theoretical sense, the study also has its implications in the field of management as well as pandemic situations.

Objective: Cerebral venous thrombosis is a common neurological disease. It has been treated so far with Warfarin that needs continuous monitoring of PT (prothrombin time) and INR (International Normalization Ratio) with potential complications due to this problem. Newer oral anticoagulants have been recommended for other thromboembolic events like pulmonary embolism and can be a good alternative treatment of CVT. We determined the effectiveness of new oral anticoagulant Rivaroxaban in patients with cerebral venous thrombosis. Material and Methods: The study was conducted in the indoor department of Neurology, Punjab Institute of neurosciences for 1year. Magnetic cerebral venography (MRV) Brain was done at the start of Rivaroxaban, at 3 months and at 6 months where needed. The bias effect was removed by getting MRV done and reported from the same department. Results: A total of 18 patients were enrolled in the study. One patient was lost to followup. Mean age of our patient data was 28.66 ± 6.66. 16 (88%) patients were females and 2 (11%) were males. 9 patients (50%) had clinical and radiological recovery based on MRV brain at 3 months.7 patients (38%) had recovery based on clinical and radiological parameters at 6 months. Only 1 patient had partial recanalization but that also improved clinically. Conclusion: Rivaroxabancan is a very effective alternative for warfarin in cerebral venous sinus thrombosis which will avoid concerns commonly encountered with warfarin.

In this paper, a novel scheme for radio resource allocation is proposed for downlink transmission in a clustered cellular network that employs orthogonal frequency division multiple access. The proposed resource allocation scheme is based on maximizing the cluster throughput, with emphasis on improving individual performance, especially those at the cell edges. The users within the cell are segregated into edge users, having strong coupling with the neighboring cells, and the center users close to the cell sites. In order to improve the data rate for the edge users, cell clustering with the joint transmission, forming virtual MIMO systems is introduced. The throughput for the center users is improved through classical opportunistic channel scheduling with interference avoidance. The frequency resources are pre-assigned to edge and center users, and the optimization problem for throughput maximization is applied separately to each so as to improve the fairness between them without affecting the overall spectrum efficiency. The simulations have been performed under various frequency reuse patterns and resource distributions within the cell–sectors to assess the performance of the proposed solution. Results indicate that clustering ensures tremendous gains in throughput per resource and spectral efficiency of edge users, while it significantly reduces scheduling complexity of the system.

This paper presents a novel technique based on Hybrid Spatial Distance Reduction Algorithm (HSDRA), to compensate the effects of deformity and mutual coupling occurred due to surface change in conformal arrays. This antenna surface deformation shifts the position of null points and loss of the main beam resulting in reduced antenna gain along with substantial undesirable effects on the antenna performance. The proposed algorithm, which cumulatively incorporates the Linearly Constraint Least Square Optimization (LCLSO) and Quadratically Constraint Least Square Optimization (QCLSO) techniques, is formulated to minimize/reduce the absolute distance between the actual (simulated/measured) radiation pattern and the desired radiation pattern while keeping the direction of main beam and nulls position under control. In particular, a 4x4 conformal microstrip phased array from planar surface is deformed to prescribe spherical-shape surface with various radii of curvature, is validated. For the enhancement of Gain of the conformal array antenna, Gain Maximization Algorithm is also proposed, the simulated results of which is compared to the traditional Phase compensation technique and unconstraint least squares optimization. The analytical results for both planar and spherical deformed configurations are first evaluated in MATLAB and then validated through Computer Simulation Technology (CST).

In this paper, the effects of mutual coupling and antenna surface deformity in a conformal wedge-shaped antenna array are compensated using a linear pattern correction technique. The problem is formulated to reduce the absolute distance between the actual (simulated) and the desired radiation patterns and to allow for null positioning control. The individual field patterns for the antenna elements are deformed due to changes in mutual coupling and the conformal surface. The deformed patterns of the individual antennas for specific bend angles are stored as lookup tables and interpolated to get the desired radiation pattern at any arbitrary bend-angle. The problem is linearly and quadratically constrained at the null points and performance compared with unconstrained optimization. The proposed solution for diminishing the effect of mutual coupling and surface deformity is independent of main lobe direction, type of individual antenna, array geometry, and spacing between antenna elements. The closed-form results are validated through Computer Simulation Technology (CST) for the wedge-shaped deformed dipole antenna array. The results for the proposed scheme are also assessed with the traditional Open Circuit Voltage Method (OCVM) and show superior compensation for deformity and the mutual coupling effects in conformal beam-forming arrays in terms of main beam direction, position and depth of nulls.

Friction Stir Welding (FSW) is a solid-state joining process for metals, non-metals and polymers. It is carried out with the help of a specially designed, non-consumable tool. The heat required, for creating a softened region at the faying surfaces, is generated by rotation of tool against the work piece material. Being a solid-state welding process, it offers several advantages like inducing minimum effect on the mechanical properties of base material, reduced shrinkage and distortion, no spatter or Ultra Violet (UV) radiations etc. However, developing a sound weld requires an appropriate combination of several process parameters e.g. the design of tool, its rotational and traversing speeds etc. To substantiate this aspect, an approach based on Response Surface Methodology (RSM) is presented during this paper that optimizes the combination of process parameters while investigating their effect on the mechanical properties of a friction stir welded butt joint configuration of Polycarbonate. To minimize the total number of combinations a Central Composite Rotatable Design (CCRD) is used with three factors and two levels. The results have shown that the butt joints fabricated at a traverse speed of 14 mm/min, rotational speed of 1700 RPM and with simple cylindrical conical tool geometry yielded the maximum ultimate tensile strength of 51.299 MPa.

Objectives: Evaluation of coagulation profile in patients with Myeloproliferative Neoplasms. Study Design: Cross sectional survey. Setting: Department of Haematology, Shaikh Zayed Hospital, Lahore. Period: From 10-03-2015 to 09-03-2016. Materials & Methods: A total of 55 patients of myeloproliferative neoplasms (Chronic Myeloid leukaemia, Polycythemia vera, Essential thrombocytosis and Primary Myelofibrosis) were studied over 1 year period. Patients of >15 yrs and both the genders were included. Patients already on anticoagulation and liver disease were excluded. All patients underwent screening of PT, APTT and D-dimer. PT and APTT was measured by KC4 Amelung coagulometer and D-Dimer by Latex Kit 60X. Results: Twenty four of 55 cases (43.64%) of myeloproliferative neoplasms had elevated Prothrombin time. Activated partial thromboplastin time was elevated in 18 of 55 (32.73%) cases and D-Dimer was elevated in 11 of 55 (20%) cases. Conclusion: CML is statistically the most spreading form of MPN and ET is least common form in comparison with other MPNs. Thrombohaemorrhagic complications significantly affect the morbidity and mortality of MPN patients which can be assessed by coagulation studies. Timely diagnosis of these prothrombotic/haemorrhagic states can decrease the morbidity in these patients

The Internet of Things (IoT) is an emerging paradigm branded by heterogeneous technologies composed of smart ubiquitous objects that are seamlessly connected to the Internet. These objects are often deployed in open environments to provide innovative services in various application domains such as smart cities, smart health, and smart communities. These IoT devices produce a massive amount of confidentiality and security-sensitive data. Thus, security of these devices is very important in order to ensure the safety and effectiveness of the system. In this paper, a decentralized authentication and access control mechanism is proposed for lightweight IoT devices and is applicable to a large number of scenarios. The mechanism is based on the technology of the fog computing and the concept of the public blockchain. The results gained from the experiments demonstrate a superior performance of the proposed mechanism when compared to a state-of-the-art blockchain-based authentication technique.

Objective: To assess depression and its associated factors among patients coming to the dental OPD of a public and a private dental college of Karachi.Methods: A cross-sectional study was carried out in public and a private dental college of Karachi from November 2018 to May 2019. The inclusion criterion of the study was being 18 years of age or above whereas those who refused to give written informed consent were excluded from the study. After checking eligibility and taking written informed consent, a total of 250 participants were included in the study using systematic random sampling. These dental outpatients were interviewed by the principal investigator with the help of a pretested structured questionnaire that contained questions about socio-demographic information, dental risk factors and questions from Patient Health Questionnaire-9. Data were analyzed on the statistical package for social sciences version 21. Inferential analysis was performed using chi-square test whereas the significance level was set at 0.05.Results: The study results revealed that 70 (28.0%) participants had minimal depression, 99 (39.6%) had mild depression, 45 (18.0%) had moderate depression, 28 (11.2%) had moderately severe depression whereas 8 (3.2%) had severe depression. Moreover, among demographic characteristics, only monthly income (p=0.037) and family history of depression (p

This paper proposes a multi-scale analysis technique based on the micromechanics of failure (MMF) to predict and investigate the damage progression and ultimate strength at failure of laminated composites. A lamina’s representative volume element (RVE) is developed to predict and calculate constituent stresses. Damages that occurred in the constituents are calculated using separate failure criteria for both fiber and matrix. Subsequently, the volume-based damage homogenization technique is utilized to prevent the localization of damage throughout the total matrix zone. The proposed multiscale analysis procedure is then used to investigate the notched and unnotched behavior of three multi-directional composite layups, [30, 60, 90, −60, 30]2S, [0, 45, 90, −45]2S, and [60, 0, −60]3S, subjected to static tension and compression loading. The specimen is fabricated from unidirectionally reinforced composite (IM7/977-3). The prediction of ultimate strength at failure and equivalent stiffness are then benchmarked against the experimental test data. The comparative analysis with various failure models is also carried out to validate the proposed model. MMF demonstrated the capability to correctly predict the ultimate strength at failure for a range of multidirectional composites laminates under tensile and compressive load. The numerically predicted findings revealed a good agreement with the experimental test data. Out of the three investigated composite layups, the simulated results for the quasi-isotropic [0, 45, 90, −45]2S layup agreed extremely well with the experimental results with all the percentage errors within 10% of the measured failure loads.

This research work explores the thermal and mass transport phenomena for a time-dependent Maxwell fluid flow in the presence of Cattaneo–Christov concept. The Maxwell fluid flow is analyzed through a stretching cylinder and sheet. Brownian motion, non-uniform heat source/sink, thermophoresis, and variable thermal conductivity are operated in this study. A theoretical analysis of the modeled system of equations is explored with the help of HAM. Impacts of fixed constraints on velocity, thermal, and concentration functions are offered graphically. It is concluded that the velocity profile heightens quickly for Newtonian fluid equated to non-Newtonian fluid (Maxwell) via curvature parameter while the temperature and concentration distributions increase quickly for non-Newtonian fluid as equated to the Newtonian fluid via curvature parameter. The presence of Maxwell and magnetic parameters increases the size of the trapping bolus.

High-Pressure Die Casting (HPDC) is one of the major production processes of automotive industry, widely used to manufacture geometrically complex nonferrous castings. The mechanical strength and microstructure of HPDC-manufactured products changes with variation in several process control parameters such as injection pressure, molten temperature, 1st and 2nd stage plunger velocity, cooling temperature, etc. Since these process parameters directly affect casting quality, their optimum combination is needed to maximize the productivity of process and minimize casting defects such as porosity, pinholes, blowholes, inclusions, etc. Hence, to tackle this problem, an approach is presented in this paper that minimizes the major casting defect, i.e., porosity, in the HPDC process by optimizing controlling parameters through Design of Experiments (DOE) in combination with a Taguchi Analysis. The results obtained showed that cooling time, injection pressure, and 2nd stage plunger velocity have a major influence on the response factor (density of the cast part). It was also concluded that, by using a 178 bar injection pressure, 665 °C molten temperature, 5 second cooling time, 210 °C mold temperature, 0.20 m·s−1 1st stage plunger velocity, and 6.0 m·s−1 2nd stage plunger velocity, the rejection rate of the selected part due to porosity was reduced by 61%.

Cerebral venous sinus thrombosis (CVST) is a rare cause of stroke that accounts for 0.5–1.0% of all strokes. Clinical presentation, predisposing factors, neuroimaging findings, and outcomes of CVST are extremely diverse, which causes a high index of suspicion in diagnosis. Therefore, early diagnosis of CVST is crucial for prompt treatment to prevent morbidity and mortality. Objective: The purpose of this prospective study is aimed at assessing the clinical characteristics, potential risk factors, and neuro-radiological features along with the topography of venous sinus involved in CVST patients in a tertiary care hospital, Lahore, Pakistan. Material and Methods: Consecutive patients enrolled in this study had a computed tomography (CT) scan, magnetic resonance imaging (MRI), and magnetic resonance venography (MRV) along with a clinical presentation to confirm the diagnosis of CVST. Categorical data were presented as percentages. Continuous variable and categorical data were compared (parenchymal lesions vs. non-parenchymal lesions) using the Student’s t-test and Chi-square test, respectively. Results: A total of 3261 patients with stroke were presented during the study period. Out of all patients, 53 confirmed patients with CVST (1.6%) were recruited; the predominant population was female (84.91%), having a male to female ratio of 1:4. Mean age of the cohort was 28.39 ± 7.19 years. Most frequent symptoms observed were headache (92.45%) followed by vomiting (75.47%), seizures (62.26%), papilledema (54.72%), visual impairment (41.51%), and altered consciousness disturbance (52.83%). The presumed risk factors associated with CVST were puerperium (52.83%), use of oral contraceptives (13.21%), antiphospholipid syndrome (7.55%), elevated serum levels of protein C and S (5.66%), and CNS infection (3.77%). On cranial CT scans, 50 patients (94.33%) showed abnormalities while 32 patients exhibited various parenchymal lesions. Seizures were more frequent in CVST patients with parenchymal lesions compared with subjects lacking parenchymal lesions. Seventy-two sinuses, either single or in combination, were involved in CVST patients, being more common in patients with parenchymal lesions than those without parenchymal lesions. The most frequent locations of CVST were the superior sagittal and transverse sinus. Conclusion: In short, non-contrast CT brain may be used as a first line investigation in suspected cases of CVST. Our study also demonstrates some regional differences in the clinical features, risk factors, and neuroimaging details of CVST as described by some other studies. Therefore, care must be taken while diagnosing and predicting the outcome of the CVST.

Distributed graph processing systems such as Pregel, PowerGraph, or GraphX gained popularity due to their superior performance of data analytics on graph-structured data. These systems employ partitioning algorithms to parallelize graph analytics while minimizing inter-partition communication. Recent partitioning algorithms, however, unrealistically assume a uniform and constant amount of data exchanged between graph vertices (i.e., uniform vertex traffic ) and homogeneous network costs between workers hosting the graph partitions. This leads to suboptimal partitioning decisions and inefficient graph processing. To this end, we developed GrapH, the first graph processing system using vertex-cut graph partitioning that considers both, diverse vertex traffic and heterogeneous network costs. The main idea is to avoid frequent communication over expensive network links using an adaptive edge migration strategy. Our evaluations show an improvement of 10 percent in graph processing latency and 60 percent in communication costs compared to state-of-the-art partitioning approaches.

With the increasing popularity of software-defined networking (SDN), ternary content-addressable memory of switches can be directly accessed by a publish/subscribe middleware to perform filtering operations at low latency. In this way, three important requirements for a publish/subscribe middleware can be fulfilled, namely, bandwidth efficiency, line-rate performance, and low latency in forwarding messages between producers and consumers. Nevertheless, it is challenging to sustain line-rate performance in the presence of dynamically changing interests of producers and consumers. In this paper, we realize a scalable, SDN-based publish/subscribe middleware, called PLEROMA, that performs efficient forwarding at line-rate. Moreover, PLEROMA offers methods to efficiently reconfigure a deployed topology in the presence of dynamic subscriptions and advertisements. We evaluate the performance of both the data plane and the control plane of PLEROMA to support our claim. Furthermore, we evaluate and benchmark the performances of SDN-compliant hardware and software switches in the context of our middleware.

This article investigates unsteady magnetohydrodynamic (MHD) mixed convective and thermally radiative Jeffrey nanofluid flow in view of a vertical stretchable cylinder with radiation absorption and heat, the reservoir was addressed. The mathematical formulation of Jeffrey nanofluid is established based on the theory of boundary layer approximations pioneered by Prandtl. The governing model expressions in partial differential equations (PDEs) form was transformed into dimensionless form via similarity transformation technique. The set of nonlinear nondimensional partial differential equations are solved with the help of the homotopic analysis method. For the purpose of accuracy, the optimizing system parameters, convergence, and stability analysis of the analytical algorithm (CSA) were performed graphically. The velocity, temperature, and concentration flow are studied and shown graphically with the effect of system parameters such as Grashof number, Hartman number, Prandtl number, thermal radiation, Schmidt number, Eckert number, Deborah number, Brownian parameter, heat source parameter, thermophoresis parameter, and stretching parameter. Moreover, the consequence of system parameters on skin friction coefficient, Nusselt number, and Sherwood number is also examined graphically and discussed.

Optimization of a manufacturing process results in higher productivity and reduced wastes. Production parameters of a local steel bar manufacturing industry of Pakistan is optimized by using six Sigma-Define, measure, analyze, improve, and controlmethodology. Production data is collected and analyzed. After analysis, experimental design result is used to identify significant factors affecting process performance. The significant factors are controlled to optimized level using two-level factorial design method. A regression model is developed that helps in the estimation of response under multi variable input values. Model is tested, verified, and validated by using industrial data collected at a local steel bar manufacturing industry of Peshawar(Khyber Pakhtunkhwa, Pakistan). The sigma level of the manufacturing process is improved to 4.01 from 3.58. The novelty of the research is the identification of the significant factors along with the optimum levels that affects the process yield, and the methodology to optimize the steel bar manufacturing process.

The grouping of parts and machines into manufacturing cells with the objective to improve grouping efficacy is the prime focus of cell formation problem. This paper presents the hybridization of simulated annealing (SA) with genetic algorithm (GA), where exploration power of GA, to broaden the search space, is combined with the intensification power of SA. A certain percentage of best solution in each generation of GA is sent, for further intensification and trajectory search, to SA. In intensification phase (with SA), Boltzmann probability distribution functions are used to allow a downhill move, with variable temperature. Starting temperature is chosen to be a small value with rapid cooling rate to keep search in a narrow region. If same solution is repeated for a certain number of generations, then shake-off is given to escape local maxima. During this shake-off, the SA parameters are relaxed to broaden the search for best neighbourhood solution. The effectiveness of proposed hybrid heuristic algorithm is evaluated through 35 benchmark problems from the literature. The GA with intensification through SA along with shake-off performed well in terms of solution quality, i.e. produced 24 best results with overall mean of 66.20. The overall comparative study shows that the proposed approach not only achieves the best solutions consistently, with minimum computational time, but also improves the results of two problems (problems 29 and 33), reporting it for the first time.

Corporate social responsibility is emerging topic in the modern business world. Employees are vital assets for any organization. Corporate Social Responsibility practices have a significant influence on employee&rsquo, s performance. The study aimed to investigate the relationship between employee perception of corporate social responsibility (CSR) and employee&rsquo, s outcome in Pakistan for SMEs. Additionally, it examined the relationship of Employee&rsquo, Perception of CSR as an independent variable. Further, this study considers mediating role of organizational justice between employee&rsquo, s perception of CSR and employee&rsquo, outcomes. The quantitative method was used to collect data from 300 SME&rsquo, s. Hypotheses were tested by using statistical software (SPSS). Correlation analysis shows the significant relationship between variables, i.e., employee&rsquo, s perception of CSR and employee outcomes. Moreover, regression analysis was performed for mediation analysis. The results show that organizational justice partially mediated between employee&rsquo, s perception of corporate social responsibility and employee&rsquo, s outcomes. Practical implications were discussed, and future research directions were recommended.

The Internet of Things (IoT) envisions a huge number of networked sensors connected to the internet. These sensors collect large streams of data which serve as input to wide range of IoT applications and services such as e-health, e-commerce, and automotive services. Complex Event Processing (CEP) is a powerful tool that transforms streams of raw sensor data into meaningful information required by these IoT services. Often these streams of data collected by sensors carry privacy-sensitive information about the user. Thus, protecting privacy is of paramount importance in IoT services based on CEP.In this paper we present a novel pattern-level access control mechanism for CEP based services that conceals private information while minimizing the impact on useful non-sensitive information required by the services to provide a certain quality of service (QoS). The idea is to reorder events from the event stream to conceal privacy-sensitive event patterns while preserving non-privacy sensitive event patterns to maximize QoS. We propose two approaches, namely an ILP-based approach and a graph-based approach, calculating an optimal reordering of events. Our evaluation results show that these approaches are effective in concealing private patterns without significant loss of QoS.

Arising user-centric graph applications such as route planning and personalized social network analysis have initiated a shift of paradigms in modern graph processing systems towards multi-query analysis, i.e., processing multiple graph queries in parallel on a shared graph. These applications generate a dynamic number of localized queries around query hotspots such as popular urban areas. However, existing graph processing systems are not yet tailored towards these properties: The employed methods for graph partitioning and synchronization management disregard query locality and dynamism which leads to high query latency. To this end, we propose the system Q-Graph for multi-query graph analysis that considers query locality on three levels. (i) The query-aware graph partitioning algorithm Q-cut maximizes query locality to reduce communication overhead. (ii) The method for synchronization management, called hybrid barrier synchronization, allows for full exploitation of local queries spanning only a subset of partitions. (iii) Both methods adapt at runtime to changing query workloads in order to maintain and exploit locality. Our experiments show that Q-cut reduces average query latency by up to 57 percent compared to static query-agnostic partitioning algorithms.

In recent years, the graph partitioning problem gained importance as a mandatory preprocessing step for distributed graph processing on very large graphs. Existing graph partitioning algorithms minimize partitioning latency by assigning individual graph edges to partitions in a streaming manner --- at the cost of reduced partitioning quality. However, we argue that the mere minimization of partitioning latency is not the optimal design choice in terms of minimizing total graph analysis latency, i.e., the sum of partitioning and processing latency. Instead, for complex and long-running graph processing algorithms that run on very large graphs, it is beneficial to invest more time into graph partitioning to reach a higher partitioning quality --- which drastically reduces graph processing latency. In this paper, we propose ADWISE, a novel window-based streaming partitioning algorithm that increases the partitioning quality by always choosing the best edge from a set of edges for assignment to a partition. In doing so, ADWISE controls the partitioning latency by adapting the window size dynamically at run-time. Our evaluations show that ADWISE can reach the sweet spot between graph partitioning latency and graph processing latency, reducing the total latency of partitioning plus processing by up to 23-47 percent compared to the state-of-the-art.

Today, workflows are executed in heterogeneous environments, such as the Internet, to automate interactions with services of business partners and other third parties. In such heterogeneous environments, device and communication failures occur frequently. These failures might delay or even stop the workflow executions rendering the workflow unavailable. For ensuring availability, workflow executions are replicated on multiple devices allowing the workflow execution to continue even if some of the devices fail. So far, two workflow replication mechanisms have been proposed: active and passive replication. While active replication incurs high cost in terms of compensation even for failure free executions, passive replication provides lower availability than active replication. In this paper, we propose a new replication mechanism called partition-tolerant replication that combines the benefits of active and passive replication. Our evaluations on OpenStack show that partition-tolerant replication imposes no compensation cost for failure free executions like passive replication while providing availability close to active replication.

Distributed Complex Event Processing (DCEP) is a paradigm to infer the occurrence of complex situations in the surrounding world from basic events like sensor readings. In doing so, DCEP operators detect event patterns on their incoming event streams. To yield high operator throughput, data parallelization frameworks divide the incoming event streams of an operator into overlapping windows that are processed in parallel by a number of operator instances. In doing so, the basic assumption is that the different windows can be processed independently from each other. However, consumption policies enforce that events can only be part of one pattern instance; then, they are consumed, i.e., removed from further pattern detection. That implies that the constituent events of a pattern instance detected in one window are excluded from all other windows as well, which breaks the data parallelism between different windows. In this paper, we tackle this problem by means of speculation: Based on the likelihood of an event's consumption in a window, subsequent windows may speculatively suppress that event. We propose the SPECTRE framework for speculative processing of multiple dependent windows in parallel. Our evaluations show an up to linear scalability of SPECTRE with the number of CPU cores., To be published in ACM/IFIP/USENIX Middleware 2017

In recent years, content-based publish/subscribe middleware has harnessed the power of Software-Defined Networking (SDN) to leverage performance gains in terms of throughput rates, end-to-end latency, etc. To this end, content filters are directly installed on the Ternary Content Addressable Memory (TCAM) of switches. Such a middleware assumes unlimited TCAM space to deploy content filters. However, in reality, TCAM is a scarce resource and the number of flow table entries available for publish/subscribe traffic is severely limited. While such a limitation poses severe problems for the deployment of publish/subscribe middleware in practice, it is yet to be addressed in literature.So, in this paper, we design a filter aggregation algorithm that merges content filters on individual switches to respect TCAM constraints while ensuring minimal increase in unnecessary network traffic. Our algorithm uses the knowledge of advertisements, subscriptions, and a global view of the network state to perform bandwidth-efficient aggregation decisions on necessary switches. We provide different flavors of this algorithm with varying degrees of accuracy and complexity and thoroughly evaluate their performances under realistic workload. Our evaluation results show that our designed aggregation algorithm successfully meets TCAM constraints on switches while also reducing unnecessary traffic introduced in the network due to aggregation as compared to a baseline approach by up to 99.9%.

With the advent of the internet of things and industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning models are trained to derive higher level knowledge. With the recent development of specialized machine learning hardware for mobile devices, a new era of distributed learning is about to begin that raises a new research question: How can we search in distributed machine learning models? Machine learning at the edge of the network has many benefits, such as low-latency inference and increased privacy. Such distributed machine learning models can also learn personalized for a human user, a specific context, or application scenario. As training data stays on the devices, control over possibly sensitive data is preserved as it is not shared with a third party. This new form of distributed learning leads to the partitioning of knowledge between many devices which makes access difficult. In this paper we tackle the problem of finding specific knowledge by forwarding a search request (query) to a device that can answer it best. To that end, we use a entropy based quality metric that takes the context of a query and the learning quality of a device into account. We show that our forwarding strategy can achieve over 95% accuracy in a urban mobility scenario where we use data from 30 000 people commuting in the city of Trento, Italy.

Distributed Complex Event Processing has emerged as a well-established paradigm to detect situations of interest from basic sensor streams, building an operator graph between sensors and applications. In order to detect event patterns that correspond to situations of interest, each operator correlates events on its incoming streams according to a sliding window mechanism. To increase the throughput of an operator, different windows can be assigned to different operator instances---i.e., identical operator copies---which process them in parallel. This implies that events that are part of multiple overlapping windows are replicated to different operator instances. The communication overhead of replicating the events can be reduced by assigning overlapping windows to the same operator instance. However, this imposes a higher processing load on the single operator instance, possibly overloading it. In this paper, we address the trade-off between processing load and communication overhead when assigning overlapping windows to a single operator instance. Controlling the trade-off is challenging and cannot be solved with traditional reactive methods. To this end, we propose a model-based batch scheduling controller building on prediction. Evaluations show that our approach is able to significantly save bandwidth, while keeping a user-defined latency bound in the operator instances., Comment: In Proceedings of ACM International Conference on Distributed and Event-Based Systems, Barcelona, Spain, June 19 - 23, 2017 (DEBS '17), 12 pages. DOI: http://dx.doi.org/10.1145/3093742.3093914

The provisioning of basic security mechanisms such as authentication and confidentiality is highly challenging in a content-based publish/subscribe system. Authentication of publishers and subscribers is difficult to achieve due to the loose coupling of publishers and subscribers. Likewise, confidentiality of events and subscriptions conflicts with content-based routing. This paper presents a novel approach to provide confidentiality and authentication in a broker-less content-based publish/subscribe system. The authentication of publishers and subscribers as well as confidentiality of events is ensured, by adapting the pairing-based cryptography mechanisms, to the needs of a publish/subscribe system. Furthermore, an algorithm to cluster subscribers according to their subscriptions preserves a weak notion of subscription confidentiality. In addition to our previous work , this paper contributes 1) use of searchable encryption to enable efficient routing of encrypted events, 2) multicredential routing a new event dissemination strategy to strengthen the weak subscription confidentiality, and 3) thorough analysis of different attacks on subscription confidentiality. The overall approach provides fine-grained key management and the cost for encryption, decryption, and routing is in the order of subscribed attributes. Moreover, the evaluations show that providing security is affordable w.r.t. 1) throughput of the proposed cryptographic primitives, and 2) delays incurred during the construction of the publish/subscribe overlay and the event dissemination.