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A land administration system (LAS) is a structured framework designed to govern the management of land resources in a specific region or country. However, LAS faces challenges like inefficiencies, a lack of transparency, and susceptibility to fraud. The digitization of land records improved efficiency but failed to address manipulation, centralized databases, and double-spending issues. Traditional lease and mortgage management systems also suffer from complexity, errors, and a lack of real-time validation. At present, a significant influx of land transactions produces substantial data, classifiable as big data due to constant minute-to-minute occurrences like land transfers, acquisitions, document verification, and leasing/mortgaging transactions. In this context, we present a Blockchain-driven system that not only tackles alteration and double-spending issues in traditional systems but also implements distributed data management. Current state-of-the-art solutions do not fully incorporate crucial features of Blockchain, such as transparency, prevention of double-spending, auditability, immutability, and user participation. To tackle this problem, this research introduces a comprehensive Blockchain-powered framework for lease and mortgage management, addressing transparency, user involvement, and double-spending prevention. Unlike existing solutions, our framework integrates key Blockchain characteristics for a holistic approach. Through practical use cases involving property owners, banks, and financial institutions, we establish a secure, distributed, and transparent method for property financing. We verify the system by employing smart contracts and assess the cost and security parameters while validating the blockchain-based mortgage and lease functions.

Mobile applications are advancing toward higher network and compute requirements, which are similar to the requirements of server applications. Users prefer to perform their tasks on the go from mobile devices instead of stationary desktop and server systems. Modern mobile applications limit mobile battery times as charging is lost quickly due to high processing and data demands. Resources other than energy are also limited in mobile devices (memory, CPU millions of instructions per second, etc.).Mobile edge computing (MEC) technologies facilitate resource-scarcemobile devices to enhance their capabilities and execute data/computeintensive applications while collaborating with resource-rich network servers to enable ubiquitous computing. On the other hand, mobile cloud computing provides higher resources for applications that have low response requirements (noninteractive applications). Bothmobile edge computing andmobile cloud computing enable technologies for Internet of Things (IoT), smart grids, and e-health applications.

The ever growing computational demands of users call for efficient cloud resource management to avoid service-level agreement (SLA) violation. Virtualization co-locates multiple virtual machines (VMs) on a single physical server to share the underlying resources for efficient resource management. However, the decision about “what” and “where” to place workloads significantly impacts performance of hosted workloads. Existing cloud schedulers consider a single resource (RAM) to co-locate workloads that as a result lead to SLA violation due to non-optimal VM placement. To handle this issue, current study has updated nova scheduler to propose a multi-resource based VM placement approach to improve application performance in terms of central processing unit (CPU) utilization and execution time. Experimentally we have shown that our proposed method has lessened application execution time by 50% when compared with one of the well-known technique.

Cloud computing emerged as one of the leading computational paradigms due to elastic resource provisioning and pay-as-you-go model. Large data centers are used by the service providers to host the various services. These data centers consume enormous energy, which leads to increase in operating costs and carbon footprints. Therefore, green cloud computing is a necessity, which not only reduces energy consumption, but also affects the environment positively. In order to reduce the energy consumption, workload consolidation approach is used that consolidates the tasks in minimum possible servers. However, workload consolidation may lead to service level agreement (SLA) violations due to non-availability of resources on the server. Therefore, workload consolidation techniques should consider the aforementioned problem. In this paper, we present two consolidation based energy-efficient techniques that reduce energy consumption along with resultant SLA violations. In addition to that, we also enhanced the existing Enhanced-Conscious Task Consolidation (ECTC) and Maximum Utilization (MaxUtil) techniques that attempt to reduce energy consumption and SLA violations. Experimental results show that the proposed techniques perform better than the selected heuristic based techniques in terms of energy, SLA, and migrations.

Smartphones are computationally constrained compared with server devices due to their size and limited battery-based power. Compute-intensive tasks are often offloaded from smartphones to high-performance computing opportunities provided by nearby high-end cloud and edge servers. ARM architectures dominate smartphones, while x86 dominate server devices. The difference in architectures requires dynamic binary translation (DBT) of compiled code migration, which increases the task execution time on the cloud servers. Multimedia applications contain a large number of vector instructions (single instruction multiple data) that are compute and resource intensive. Vector instructions optimize application execution by parallel processing multiple data points in a single instruction. However, DBT of vector instructions losses the parallelism and optimization due to vector-scalar translations. We present and analyze a framework for pre-compiled vector instruction translation and offloading in heterogeneous compute architectures that avoids the execution overhead of compiled code offloading. The framework maps and translates ARM vector intrinsics to x86 vector intrinsics such that an application programmed for ARM architecture can be executed on the x86 architecture without any modification. We analyze the code offloading framework with static code analysis to determine the optimal compilers and corresponding compilation parameters. Moreover, we analyze the overhead of the vector instruction translator and application profiler. Furthermore, the comparative analysis based on increasing computational sizes reveals that our framework provides 78.8% energy efficiency as compared with existing code translation and offloading frameworks.