In a memory-managed programming language, programmers allocate memory by creating new objects, but programmers never free memory. A garbage collector (GC) periodically reclaims memory used by unreachable objects. As an optimization based on escape analysis, some memory can be freed explicitly by instructions inserted by the compiler. This optimization reduces the cost of garbage collection, without changing the programming model. We designed and implemented this explicit freeing optimization for the Go language. We devised a new escape analysis that is both powerful and fast (𝑂(𝑁^2) time). Our escape analysis identifies short-lived heap objects that can be safely explicitly deallocated. We also implemented a freeing primitive that is safe for use in concurrent environments. We evaluated our system, GoFree, on 6 open-source Go programs. GoFree did not observably slow down compilation. At run time, GoFree deallocated on average 14% of allocated heap memory. It reduced GC frequency by 7%, GC time by 13%, wall-clock time by 2%, and heap size by 4%. We open-source GoFree.

Wheeled mobile manipulators (WMMs) involving a wheeled mobile platform and a serial manipulator are finding increasing applications in diverse fields, creating new challenges in performing high-precision operations in a spacious workspace. WMMs are challenging to control due to uncertainties in system parameters, coupled dynamics, and external disturbances, which make stability guarantees difficult. This paper proposes a virtual decomposition control (VDC)-based trajectory tracking controller for WMMs, addressing joint flexibility, external disturbances, etc. The proposed method uses a VDC-based iterative approach to manage the complex coupled dynamics and employs a separate adaptive controller to handle joint flexibility. The robotic system’s stability is validated using the specific features of VDC (proof of each subsystem’s virtual stability) according to the Lyapunov stability theory. The advantages and effectiveness of the proposed method are demonstrated through experiments.Note to Practitioners—This paper addresses the challenges faced in controlling WMMs, which are becoming increasingly common in various industrial and service applications due to their ability to perform tasks in large and dynamic environments. The coupling between the wheeled platform and the manipulator, as well as uncertainties in system parameters such as joint flexibility and external disturbances, make precise trajectory tracking difficult. To address these challenges, this paper presents a control approach based on VDC, which breaks down the complex system into manageable subsystems and ensures stability for each part individually. The control strategy also incorporates adaptive control to handle joint flexibility and unpredictable disturbances. The stability of the system is rigorously proven through Lyapunov theory, ensuring robust performance under real-world conditions. Practitioners working on autonomous mobile robots equipped with manipulators may find this approach useful for improving trajectory tracking performance in uncertain and dynamic environments. However, the practical implementation of this method will require careful tuning of controller parameters and real-time computational capabilities to ensure seamless operation in real applications.

Escape analysis plays a crucial role in garbage-collected languages as it enables the allocation of non-escaping variables on the stack by identifying the dynamic lifetimes of objects and pointers. This helps in reducing heap allocations and alleviating garbage collection pressure. However, Go, as a garbage-collected language, employs a fast yet conservative escape analysis, which is field-insensitive and omits point-to-set calculation to expedite compilation. This results in more variables being allocated on the heap. Empirical statistics reveal that field access and indirect memory access are prevalent in real-world Go programs, suggesting potential opportunities for escape analysis to enhance program performance. In this paper, we propose MEA 2 , an escape analysis framework atop GoLLVM (an LLVM-based Go compiler), which combines field sensitivity and points-to analysis. Moreover, a novel generic function summary representation is designed to facilitate fast inter-procedural analysis. We evaluated it by using MEA 2 to perform stack allocation in 12 wildly-use open-source projects. The results show that, compared to Go’s escape analysis, MEA 2 can reduce heap allocation sites by 7.9 % on average (up to 25.7 % ) while reducing the dynamic memory allocation size by 11.6 % on average (up to 35.5 % ). All this is achieved while keeping the time overhead of escape analysis within 1 % of the compilation process.

CGO is a foreign function interface mechanism that enables the creation of Go packages that call C code. It provides a way to reuse legacy C code or high-performance C libraries. However, it is tedious and error-prone to manually write the bindings of C libraries in Go. To make better use of CGO to realize the use of C libraries in Go, we propose CGORewritter in this paper. For a given Go library and corresponding C library, the internal code of the Go API function can be rewritten while keeping the Go API unchanged, and the C API function can be called through CGO in a semi-automatic way so that the application layer program can upgrade without any change. We use this method to rewrite the go/crypto with the OpenSSL library. Experimental results show that the rewritten code maintains full functionality and can be used by application code without modification. Besides, the rewritten code gains a 0.97-3.07× speedup.

Demand for enterprise data warehouse solutions to support real-time Online Transaction Processing (OLTP) queries as well as long-running Online Analytical Processing (OLAP) workloads is growing. Greenplum database is traditionally known as an OLAP data warehouse system with limited ability to process OLTP workloads. In this paper, we augment Greenplum into a hybrid system to serve both OLTP and OLAP workloads. The challenge we address here is to achieve this goal while maintaining the ACID properties with minimal performance overhead. In this effort, we identify the engineering and performance bottlenecks such as the under-performing restrictive locking and the two-phase commit protocol. Next we solve the resource contention issues between transactional and analytical queries. We propose a global deadlock detector to increase the concurrency of query processing. When transactions that update data are guaranteed to reside on exactly one segment we introduce one-phase commit to speed up query processing. Our resource group model introduces the capability to separate OLAP and OLTP workloads into more suitable query processing mode. Our experimental evaluation on the TPC-B and CH-benCHmark benchmarks demonstrates the effectiveness of our approach in boosting the OLTP performance without sacrificing the OLAP performance.

Traditionally resource utilization on physical servers in cloud data center is uncertain. On one hand, resources will be wasted if the assignment of tasks are not enough. On the other hand it will cause overload if the assignment of tasks are too much. This is especially obvious when the applications are the same type. To solve this issue and considering CPU intensive application is one of the most common type of application in cloud, we have studied the optimization strategy for this kind of applications on the same server. According to resource preferences of different types of applications, we analyze the case that multiple CPU intensive applications run simultaneously, and put forward a model which can make a prediction of execution time for this case. Extensive experiments show that the model is suitable for CPU intensive applications, and it can accurately predict their execution time. In order to improve the execution efficiency of applications, we propose a scheduling model for CPU intensive applications. Experiments show that the scheduling model can improve the execution efficiency of applications effectively and optimize the resource utilization.

Energy consumption is very important to cloud data centers, as it takes a large quotient of the operation cost much related to the environment. To decrease the energy consumption in cloud data center, one possible solution is processing different types of applications in clouds with different strategies. However, to reach this goal, the prime question to be solved is how to effectively classify the type of cloud applications. To solve the problem, this paper uses the method to monitor the resource usage of different applications in clouds, and get the resource usage parameters of different applications. Through analysis, we find the main parameters, which can distinguish different types of applications. Using these parameters we draw out the features of the applications and establish a model to classify different cloud applications. Extensive experiments show that the model put forward can effectively and accurately classify CPU intensive application, I/O intensive application and network intensive application. It can be used as a basis on how to high efficiently use the cloud resources.