Almost all of the current process scheduling algorithms which are used in modern operating systems (OS) have their roots in the classical scheduling paradigms which were developed during the 1970's. But modern computers have different types of software loads and user demands. We think it is important to run what the user wants at the current moment. A user can be a human, sitting in front of a desktop machine, or it can be another machine sending a request to a server through a network connection. We think that OS should become intelligent to distinguish between different processes and allocate resources, including CPU, to those processes which need them most. In this work, as a first step to make the OS aware of the current state of the system, we consider process dependencies and interprocess communications. We are developing a model, which considers the need to satisfy interactive users and other possible remote users or customers, by making scheduling decisions based on process dependencies and interprocess communications. Our simple proof of concept implementation and experiments show the effectiveness of this approach in the real world applications. Our implementation does not require any change in the software applications nor any special kind of configuration in the system, Moreover, it does not require any additional information about CPU needs of applications nor other resource requirements. Our experiments show significant performance improvement for real world applications. For example, almost constant average response time for Mysql data base server and constant frame rate for mplayer under different simulated load values.
work called RSIO @cite_4 has the most similarities with our work. RSIO looks at process I O patterns as a way of detecting interactive processes. It also tries to identify other processes involved in a user activity and provide a scheduling policy to improve interactive performance. This policy is based on access patterns to I O devices. RSIO needs a configuration file that defines which I O devices should be monitored to detect interactive processes. It has also a relatively complicated heuristic mechanism to detect processes involved in a user interaction.
@cite_4
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We present RSIO, a processor scheduling framework for improving the response time of latency-sensitive applications by monitoring accesses to I O channels and inferring when user interactions occur. RSIO automatically identifies processes involved in a user interaction and boosts their priorities at the time the interaction occurs to improve system response time. RSIO also detects processes indirectly involved in processing an interaction, automatically accounting for dependencies and boosting their priorities accordingly. RSIO works with existing schedulers and requires no application modifications to identify periods of latency-sensitive application activity. We have implemented RSIO in Linux and measured its effectiveness on microbenchmarks and real applications. Our results show that RSIO is easy to use and can provide substantial improvements in system performance for latency-sensitive applications. The I O access patterns of parallel programs often consist of accesses to a large number of small, noncontiguous pieces of data. If an application's I O needs are met by making many small, distinct I O requests, however, the I O performance degrades drastically. To avoid this problem, MPI-IO allows users to access a noncontiguous data set with a single I O function call. This feature provides MPI-IO implementations an opportunity to optimize data access. We describe how our MPI-IO implementation, ROMIO, delivers high performance in the presence of noncontiguous requests. We explain in detail the two key optimizations ROMIO performs: data sieving for noncontiguous requests from one process and collective I O for noncontiguous requests from multiple processes. We describe how one can implement these optimizations portably on multiple machines and file systems, control their memory requirements, and also achieve high performance. We demonstrate the performance and portability with performance results for three applications--an astrophysics-application template (DIST3D), the NAS BTIO benchmark, and an unstructured code (UNSTRUC)--on five different parallel machines: HP Exemplar, IBM SP, Intel Paragon, NEC SX-4, and SGI Origin2000. For many scientific applications, significant time is spent in tuning codes for a particular high-performance architecture. Tuning approaches range from the relatively nonintrusive (e.g., by using compiler options) to extensive code modifications that attempt to exploit specific architecture features. Intrusive techniques often result in code changes that are not easily reversible, and can negatively impact readability, maintainability, and performance on different architectures. We introduce an extensible annotation-based empirical tuning system called Orio that is aimed at improving both performance and productivity. It allows software developers to insert annotations in the form of structured comments into their source code to trigger a number of low-level performance optimizations on a specified code fragment. To maximize the performance tuning opportunities, the annotation processing infrastructure is designed to support both architecture-independent and architecture-specific code optimizations. Given the annotated code as input, Orio generates many tuned versions of the same operation and empirically evaluates the alternatives to select the best performing version for production use. We have also enabled the use of the Pluto automatic parallelization tool in conjunction with Orio to generate efficient OpenMP-based parallel code. We describe our experimental results involving a number of computational kernels, including dense array and sparse matrix operations. Many proposals for managing replicated data use sites running the Snapshot Isolation (SI) concurrency control mechanism, and provide 1-copy SI or something similar, as the global isolation level. This allows good scalability, since only ww-conflicts need to be managed globally. However, 1-copy SI can lead to data corruption and violation of integrity constraints [5]. 1-copy serializability is the global correctness condition that prevents data corruption. We propose a new algorithm Replicated Serializable Snapshot Isolation (RSSI) that uses SI at each site, and combines this with a certification algorithm to guarantee 1-copy serializable global execution. Management of ww-conflicts is similar to what is done in 1-copy SI. But unlike previous designs for 1-copy serializable systems, we do not need to prevent all rw-conflicts among concurrent transactions. We formalize this in a theorem that shows that many rw-conflicts are indeed false positives that do not risk non-serializable behavior. Our proposed RSSI algorithm will only abort a transaction when it detects a well-defined pattern of two consecutive rw-edges in the serialization graph. We have built a prototype that integrates our RSSI with the existing open-source PostgresR(SI) system. Our performance evaluation shows that there is a worst-case overhead of about 15 for getting full 1copy serializability as compared to 1-copy SI in a cluster of 8 nodes, with our proposed RSSI clearly outperforming the previous work [6] for update-intensive workloads.