A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow

A Survey of Techniques for Improving Security of GPUs

Graphics processing unit (GPU), although a powerful performance-booster, also has many security vulnerabilities. Due to these, the GPU can act as a safe-haven for stealthy malware and the weakest `link' in the security `chain'. In this paper, we present a survey of techniques for analyzing and improving GPU security. We classify the works on key attributes to highlight their similarities and differences. More than informing users and researchers about GPU security techniques, this survey aims to increase their awareness about GPU security vulnerabilities and potential countermeasures

Efficient implementation of hierarchical resource control for multi-agent systems

Development of the World Wide Web makes it possible for multiple computers to work together in order to solve problems and make the most efficient use of resources. A distributed system is composed of such computers which are separately located and connected with each other through a network. One paradigm for computation using distributed systems is the multi-agent systems, in which many autonomous agents interact with each other to solve problems. The agents in a multi-agent system may be distributed on different computers (or nodes), where each computer owns its resources. Although the resources in a multi-agent system are connected by a network through which mobile agents can migrate for accessing sufficient resources, how to share these independently owned resources in both an effective and an efficient way is not fully understood. A key challenge in multi-agent systems is how to account for and control the resources which are located on individual nodes. The CyberOrgs model offers one approach to manage resources among competitive or collaborative agents by organizing computations and resources in a hierarchy. A cyberorg encapsulates agents and resources in a boundary and distributes the resources available to it within this boundary. A cyberorg contained in another cyberorg has a contract with the outer cyberorg, according to which it receives resources that it may use. A cyberorg also encapsulates an amount of the eCash, which is the currency for purchasing resources from its host cyberorg. Therefore, cyberorgs have a hierarchical structure in which resources are delivered to computations by a process where resources flow down from the root to the leaves of the hierarchy and the eCash flows up from the leaves toward the root. However, the hierarchical structure of the CyberOrgs model presents challenges in scalability. As a result, efficiency is an important concern in the implementation of CyberOrgs. In this thesis, an efficient implementation of the CyberOrgs model is described. System design, APIs of the implementation, example applications, experimental results, and future directions are presented

An energy optimization with improved QOS approach for adaptive cloud resources

In recent times, the utilization of cloud computing VMs is extremely enhanced in our day-to-day life due to the ample utilization of digital applications, network appliances, portable gadgets, and information devices etc. In this cloud computing VMs numerous different schemes can be implemented like multimedia-signal-processing-methods. Thus, efficient performance of these cloud-computing VMs becomes an obligatory constraint, precisely for these multimedia-signal-processing-methods. However, large amount of energy consumption and reduction in efficiency of these cloud-computing VMs are the key issues faced by different cloud computing organizations. Therefore, here, we have introduced a dynamic voltage and frequency scaling (DVFS) based adaptive cloud resource re-configurability (ACRR) technique for cloud computing devices, which efficiently reduces energy consumption, as well as perform operations in very less time. We have demonstrated an efficient resource allocation and utilization technique to optimize by reducing different costs of the model. We have also demonstrated efficient energy optimization techniques by reducing task loads. Our experimental outcomes shows the superiority of our proposed model ACRR in terms of average run time, power consumption and average power required than any other state-of-art techniques

SQUASH: Simple QoS-Aware High-Performance Memory Scheduler for Heterogeneous Systems with Hardware Accelerators

Modern SoCs integrate multiple CPU cores and Hardware Accelerators (HWAs) that share the same main memory system, causing interference among memory requests from different agents. The result of this interference, if not controlled well, is missed deadlines for HWAs and low CPU performance. State-of-the-art mechanisms designed for CPU-GPU systems strive to meet a target frame rate for GPUs by prioritizing the GPU close to the time when it has to complete a frame. We observe two major problems when such an approach is adapted to a heterogeneous CPU-HWA system. First, HWAs miss deadlines because they are prioritized only close to their deadlines. Second, such an approach does not consider the diverse memory access characteristics of different applications running on CPUs and HWAs, leading to low performance for latency-sensitive CPU applications and deadline misses for some HWAs, including GPUs. In this paper, we propose a Simple Quality of service Aware memory Scheduler for Heterogeneous systems (SQUASH), that overcomes these problems using three key ideas, with the goal of meeting deadlines of HWAs while providing high CPU performance. First, SQUASH prioritizes a HWA when it is not on track to meet its deadline any time during a deadline period. Second, SQUASH prioritizes HWAs over memory-intensive CPU applications based on the observation that the performance of memory-intensive applications is not sensitive to memory latency. Third, SQUASH treats short-deadline HWAs differently as they are more likely to miss their deadlines and schedules their requests based on worst-case memory access time estimates. Extensive evaluations across a wide variety of different workloads and systems show that SQUASH achieves significantly better CPU performance than the best previous scheduler while always meeting the deadlines for all HWAs, including GPUs, thereby largely improving frame rates

Decentralised Workload Scheduler for Resource Allocation in Computational Clusters

This paper presents a detailed design of a decentralised agent-based scheduler, which can be used to manage workloads within the computing cells of a Cloud system. Our proposed solution is based on the concept of service allocation negotiation, whereby all system nodes communicate between themselves, and scheduling logic is decentralised. The presented architecture has been implemented, with multiple simulations run using real-world workload traces from the Google Cluster Data project. The results were then compared to the scheduling patterns of Google’s Borg system