Modern vehicles are equipped with high-performance compute systems. These compute resources mostly stay idle as most of the time vehicles get parked in the parking lots. In this work, we propose to utilize the unused compute resources of the vehicles efficiently to enhance the computing power of regular cloud systems , which is termed as vehicular cloud . Unlike in traditional cloud computing resources, the vehicles or vehicular compute resources move in or out of the parking lot, which introduces dynamic nature of the available compute resources. This makes it challenging for the vehicular cloud to ensure reliability of execution of the user-submitted tasks. In this work, we propose an approach to maximize the profit of the vehicular cloud by ensuring the reliability of the vehicular cloud. We consider user-submitted tasks with execution time , deadline and revenue associated with it. Our approach classifies the tasks based on the deadline, and orders the tasks for task admission based on the expected profit of the task. We also perform the classification of available vehicular units based on the expected residency time of vehicles and use the same for allocating vehicular units for redundant execution of task to ensure higher reliability. As the task execution time has a direct impact on redundancy requirements to ensure higher reliability, we convert the longer tasks to a chain of shorter sub-tasks to reduce the redundancy requirement. Our experiments show that the proposed approach outperforms the state-of-the-art approach with a profit margin increasing up to 25 to 45 % in real-life scenarios.The codes and dataset for this work are available at our https://github.com/SuvarthiSarkar/Efficient-profit-maximization-in-reliability-concerned-static-vehicular-cloud-system.git GitHub repository.

Nowadays the cloud system receives requests from a wide horizon of users. In order to execute a large number of modern resource-intensive, latency-sensitive applications with deadline requests from the users, the cloud systems are equipped with powerful machines, and the machines run for a significant amount of time. This leads to an increase in the probability of failures of these machines. Hence, the reliability of the cloud system is to be duly considered while designing a scheduling strategy for executing resource-intensive, latency-sensitive applications on it. This paper proposes an efficient scheduling strategy for executing real-time applications (scientific applications) maintaining the reliability constraints of both the cloud system and applications and the deadline constraints of these applications. The proposed policy assigns recoveries for an optimal number of tasks of the application while scheduling them on the cloud considering the reliability constraints of both the cloud system and the application. The experimental evaluation proves that the proposed policy outperforms the state-of-the-art policy both for the synthetic task set and scientific workflows.

Multi-tier edge-cloud storage systems are a growing trend, particularly with the rise of cloud computing and big data. It allows data to be stored across multiple servers instead of relying on a single server or location. However, centralized cloud storage services face unpredictable latency issues when dealing with huge traffic of data retrieval requests. A user connects to the system through subscription fees and initiates data content read requests. All the requests associated with different amounts of profit and deadlines depending on the request generated user's subscription fees. The number of requests can vary significantly with time, with specific data access requests being more frequent while others are outliers. These dynamics pose obstacles to achieving a fast and efficient data access system. To tackle this issue, edge-cloud storage emerged as an effective solution, which involves deploying storage servers equipped with private and public caches closer to user's geographical locations. To enhance the system's efficiency, these storage servers form a coalition and share data among themselves. In our work, we focused on addressing the problem of maximizing profits in a multi-tier cloud-edge storage system. We proposed an efficient approach that maximizes the total profit of the system through (a) efficient collaboration among edge servers, (b) splitting the data storage space in each edge server into private and public components in a nice ratio, and (c) deploying an efficient data replacement policy. To evaluate the effectiveness of our approach, we compared it to other state-of-the-art approaches on both synthetic datasets and real-life datasets. The results demonstrated that our approach led to significant increases in profit up to 21 % and 19.3% for synthetic datasets and real-life datasets respectively.

High processing capabilities of today's large systems are also used for real time applications, where executing tasks before their deadline is essential. On the other hand, with increase in the processing capability, energy consumption also increases for such systems. Thus energy efficient execution of real time tasks in such large systems has found to be promising research area in recent time. Scheduling tasks in such large systems using only low level power construct like DVFS is not efficient. In this paper, we have exploited the power consumption pattern of the recent commercial processors and derived a simple power model with a higher granularity for systems have large number of processor with each processor having multi-threading feature. We have then proposed an energy efficient scheduling technique namely, smart allocation policy for executing a set of aperiodic independent real time tasks on large system such that no task misses it deadline. We have analyzed the instantaneous power consumption and the overall energy consumption of the proposed policy along with other five baseline policies for a wide variety of synthetic data sets and real trace data. As execution time of tasks has a significant impact on scheduling and on the overall performance of the system, we have considered six different execution time models of task for our experiment. Experimental evaluation reveals that our proposed policy performs significantly better than baseline policies for all the variations of synthetic data and for real trace data.

Classical work stealing is an efficient dynamic load-balancing technique in shared memory multiprocessor or multicore system. But the performance of the same classical work scheduling on cluster chip multicore is not appreciable. So modification to this is necessary to improve performance. In this paper, we have discussed many earlier proposed modifications, and also proposed some simplistic modifications to suite targeted clustered environment. We have described a methodology to evaluate all the variations of work stealing analytically and experimentally on multiprocessor simulator and on real platform. Our methodology of evaluation include designing of novel parametric synthetic benchmark, which can be used to mimic behavior (or profile) of many real life benchmarks. The designed synthetic benchmark caters a wide range of application profiles to evaluate the design space of both variations of work stealing algorithms and clustered chip multiprocessor. In this work, we found that if the number of available parallelism of the targeted application is higher and data sharing between tasks is high then one of the proposed modification of work stealing (probabilistic based victim search and threshold on size of migratable task) outperform the rest of the modifications.

Performance of 3D stacked memory is impressive in multicore system. In three dimensional stacked large chip multiprocessor (3D LCMP), memory and memory network are integrated on top of the processors and processor network. In this paper, we have proposed an online strategy for mapping application's tasks and data onto 3D LCMP platforms. To meet performance constraints every application demand a set of resources (may be number of processor and amount of memory). An important criteria in allocation is to allocate all required resources as near as possible to reduce the communication overhead. We have proposed approximate nearest approach to allocate resources of a layer with respect to other resource layer (either processor or memory). In our work, we have compared three different strategies to allocate resources to application: in first one processor allocation is preferred over memory allocation, in second one memory allocation is preferred over processor and in third one priority is set depend on requirement of applications. Our experimental analysis shows 21% in average and up to 30% improvement over state of art one layer resource allocation. On demand priority based resource allocation improve 26% over simple processor or memory priority based resource allocation.

As almost all applications run-time characteristics exhibit time varying phase behavior. So scheduling and binding strategy considering this behavior of applications plays an important role in achieving high throughput and less power consumption. In this paper, we have considered binding of already scheduled multiphase application on to linear multicore architecture. This approaches bind the scheduled applications on nearby cores and hence reduces the overall data movement. We have modeled over all data communication overhead of application on a linear architecture and use this model in binding. Also we have proposed and evaluated four different approaches for binding the multi-phase applications on linear multicore architecture. The proposed approach are (a) random iterative refinement, (b) biggest block left-right approach (c) biggest block center-center approach and (d) hierarchical binding using perfect minimum cost matching. Result shows that hierarchical binding using minimum cost perfect matching based approach outperform rest of the approaches.

This paper deals with a methodology for software estimation to enable design space exploration of heterogeneous multiprocessor systems. Starting from fork-join representation of application specification along with high level description of multiprocessor target architecture and mapping of application components onto architecture resource elements, it estimates the performance of application on target multiprocessor architecture. The methodology proposed includes the effect of basic compiler optimizations, integrates light weight memory simulation and instruction mapping for complex instruction to improve the accuracy of software estimation. To estimate performance degradation due to contention for shared resources like memory and bus, synthetic access traces coupled with interval analysis technique is employed. The methodology has been validated on a real heterogeneous platform. Results show that using estimation it is possible to predict performance with average errors of around 11%.