Raja Wasim Ahmad

A survey on virtual machine migration and server consolidation frameworks for cloud data centers

Modern Cloud Data Centers exploit virtualization for efficient resource management to reduce cloud computational cost and energy budget. Virtualization empowered by virtual machine (VM) migration meets the ever increasing demands of dynamic workload by relocating VMs within Cloud Data Centers. VM migration helps successfully achieve various resource management objectives such as load balancing, power management, fault tolerance, and system maintenance. However, being resource-intensive, the VM migration process rigorously affects application performance unless attended by smart optimization methods. Furthermore, a Cloud Data Centre exploits server consolidation and DVFS methods to optimize energy consumption. This paper reviews state-of-the-art bandwidth optimization schemes, server consolidation frameworks, DVFS-enabled power optimization, and storage optimization methods over WAN links. Through a meticulous literature review of state-of-the-art live VM migration schemes, thematic taxonomies are proposed to categorize the reported literature. The critical aspects of virtual machine migration schemes are investigated through a comprehensive analysis of the existing schemes. The commonalties and differences among existing VM migration schemes are highlighted through a set of parameters derived from the literature. Finally, open research issues and trends in the VM migration domain that necessitate further consideration to develop optimal VM migration schemes are highlighted.

Energy Efficient Computational Offloading Framework for Mobile Cloud Computing

The latest developments in mobile computing technology have changed user preferences for computing. However, in spite of all the advancements in the recent years, Smart Mobile Devices (SMDs) are still low potential computing devices which are limited in memory capacity, CPU speed and battery power lifetime. Therefore, Mobile Cloud Computing (MCC) employs computational offloading for enabling computationally intensive mobile applications on SMDs. However, state-of-the-art computational offloading frameworks lack of considering the additional overhead of components migration at runtime. Therefore resources intensive and energy consuming distributed application execution platform is established. This paper proposes a novel distributed Energy Efficient Computational Offloading Framework (EECOF) for the processing of intensive mobile applications in MCC. The framework focuses on leveraging application processing services of cloud datacenters with minimal instances of computationally intensive component migration at runtime. As a result, the size of data transmission and energy consumption cost is reduced in computational offloading for MCC. We evaluate the proposed framework by benchmarking prototype application in the real MCC environment. Analysis of the results show that by employing EECOF the size of data transmission over the wireless network medium is reduced by 84 % and energy consumption cost is reduced by 69.9 % in offloading different components of the prototype application. Hence, EECOF provides an energy efficient application layer solution for computational offloading in MCC.

A Review on mobile application energy profiling

The shift of the information access paradigm to a mobile platform motivates research in mobile application energy profiling to augment device battery lifetime. Energy profiling schemes estimate mobile application power consumption when it is executed on resource-constrained mobile devices. Accurate power estimation?helps identify rogue applications to optimize mobile battery power usage. The lack of a comprehensive survey on mobile application energy profiling schemes that covers?various energy profiling aspects, such as profiling?granularity,?types, measurement resources,?and?model flexibility, has motivated us to review the existing literature comprehensively. Application energy profiling?schemes exploit either hardware-equipment or software-based solutions to track battery-draining behavior during application execution in mobile devices. This study comprehensively reviews state-of-the-art mobile application energy profiling schemes to?investigate the strengths and weaknesses of existing schemes. We?propose a detailed thematic taxonomy based on the extensive literature review on mobile application energy profiling to classify the existing literature. The critical aspects and related features of existing energy profiling schemes are examined through an exhaustive qualitative analysis. The?significant parameters from the reported literature are also extracted to investigate commonalities and differences among existing schemes. Finally, several research issues in mobile application energy profiling are put forward that should be addressed to increase energy profiling strength.

A lightweight distributed framework for computational offloading in mobile cloud computing.

The latest developments in mobile computing technology have enabled intensive applications on the modern Smartphones. However, such applications are still constrained by limitations in processing potentials, storage capacity and battery lifetime of the Smart Mobile Devices (SMDs). Therefore, Mobile Cloud Computing (MCC) leverages the application processing services of computational clouds for mitigating resources limitations in SMDs. Currently, a number of computational offloading frameworks are proposed for MCC wherein the intensive components of the application are outsourced to computational clouds. Nevertheless, such frameworks focus on runtime partitioning of the application for computational offloading, which is time consuming and resources intensive. The resource constraint nature of SMDs require lightweight procedures for leveraging computational clouds. Therefore, this paper presents a lightweight framework which focuses on minimizing additional resources utilization in computational offloading for MCC. The framework employs features of centralized monitoring, high availability and on demand access services of computational clouds for computational offloading. As a result, the turnaround time and execution cost of the application are reduced. The framework is evaluated by testing prototype application in the real MCC environment. The lightweight nature of the proposed framework is validated by employing computational offloading for the proposed framework and the latest existing frameworks. Analysis shows that by employing the proposed framework for computational offloading, the size of data transmission is reduced by 91%, energy consumption cost is minimized by 81% and turnaround time of the application is decreased by 83.5% as compared to the existing offloading frameworks. Hence, the proposed framework minimizes additional resources utilization and therefore offers lightweight solution for computational offloading in MCC.

Greening emerging IT technologies: techniques and practices

The tremendous increase in global industrial activity has resulted in high utilization of natural energy resources and increase in global warming over the last few decades. Meanwhile, computing has become a popular utility of modern human lifestyle. With the increased popularity of computing and IT services, the corresponding energy consumption of the IT industry has also increased rapidly. The computing community realizes the importance of green measures and provides technological solutions that lead to its energy-aware operations along with facilitating the same in other IT enabled industries. Green and sustainable computing practices review the environmental impact of the computing industry to encourage the adoption of practices and technologies for efficient operations. “Green Computing” paradigm advocates the energy-proportional and efficient usage of computing resources in all emerging technologies, such as Big Data and Internet of Things (IoT). This article presents a review of green computing techniques amidst the emerging IT technologies that are evident in our society. The best practices for green computing and the trade-off between green and high-performance policies is debated. Further, we discuss the imminent challenges facing the efficient green operations of emerging IT technologies.

Computational offloading mechanism for native and android runtime based mobile applications

We empirically evaluate the current state of the computational offloading categories.We identified research gap in computational offloading with the introduction of ART.We propose an offloading framework for native and ART based mobile application.We provide the proof of concept experiment which validates the proposed framework. Mobile cloud computing is a promising approach to augment the computational capabilities of mobile devices for emerging resource-hungry mobile applications. Android-based smartphones have opened real-world venues for mobile cloud applications mainly because of the open source nature of Android. Computational offloading mechanism enables the augmentation of smartphone capabilities. The problem is majority of existing computational offloading solutions for Android-based smartphones heavily depends on Dalvik VM (an application-level VM). Apart from being a discontinued product, Dalvik VM consumes extra time and energy because of the just-in-time (JIT) compilation of bytecode into machine instructions. With regard to this problem, Google has introduced Android Runtime (ART) featuring ahead-of-time (AHOT) compilation to native instructions in place of Dalvik VM. However, current state-of-the-art offloading solutions do not consider AHOT compilations to native binaries in the ART environment. To address the issue in offloading ART-based mobile applications, we propose a computational offloading framework. The proposed framework requires infrastructural support from cloud data centers to provide offloading as a service for heterogeneous mobile devices. Numerical results from proof-of-concept implementation revealed that the proposed framework improves the execution time of the experimental application by 76% and reduces its energy consumption by 70%.

Analysis of Vector Code Offloading Framework in Heterogeneous Cloud and Edge Architectures

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.

Cloud monitoring: A review, taxonomy, and open research issues

Abstract Cloud monitoring supervise and manages the operational work-flow and processes within cloud data centers to ensure its performance capacity and capabilities. It assists smooth running of cloud services and minimizes the probability of SLA violation. Based on the requirements of cloud users and providers, it has various aspects, purposes, and utilization. For instance, a cloud provider exploits a monitoring tool to efficiently utilize underlying resources of a cloud. The unavailability of a comprehensive survey covering various aspects of cloud monitoring including purposes, communication models, performance overhead, scalability and architectural designs motivated to review this topic. This paper comprehensively reviews state-of-the-art cloud monitoring solutions for private and public clouds. It proposes a thematic taxonomy to classify the existing cloud monitoring solutions based on a set of parameters. It proposes a detailed analysis of existing solutions based on the proposed thematic taxonomy to highlight the commonalities and differences in existing solutions. Lastly, it puts forward a set of open research issues in this domain of research that hinders proposing optimal cloud monitoring solutions. This paper will help researchers of this domain to understand the problems clearly in this research area.

Online Cloud-Based Battery Lifetime Estimation Framework for Smartphone Devices

Abstract Smartphones are resource constrained external battery operated devices. The resources of smartphone devices such as a battery, CPU, and RAM are very low compared to the desktop server. However, the requirements of smartphone users are growing tremendously. As a result, smartphone applications perform rich functionality to enrich user experience. However, due to increase in the execution capacity of smartphone applications, smartphone battery lifetime minimizes. This study proposes a cloud-based framework that estimates battery lifetime of a smartphone device. Moreover, it overviews the theoretical design of computational offloading framework to present an application area for the proposed work. Finally, it presents preliminary results to evaluate the proposed framework.

Adaptive Opportunistic Routing over DTMANETS: Proposals and Issues

The convenience of small, cheep, and mobile communication devices such as laptops, cell phones, handheld devices, and mobiles sensor nodes, has popularized mobile ad hoc networks (MANETs). With the convenience, interconnection among these devices introduced new dimensions of challenges for the technology to be used for communication. Such challenges include wireless communication, mobility, and portability. Furthermore, the sparse behavior of nodes in turbulent areas, where connectivity is commonly not possible all the time, resulted in yet another exciting technology known as delay tolerant networks (DTNs). This work is related to the association of opportunistic techniques with different scenarios in which different opportunistic elements of relay nodes, e.g. message storage capacity, territory and velocity are classified according to its usefulness in a given scenario.